I am not sure if this is what you are seeking, but here are a few comments.
When I teach an introductory JMP class some of the key ponts I make include:
Then another key point is to introduce stacked tables great for By analyses (Anova) vs. unstacked used for modeling and correlation. This fits the rubric of organizing or data prep for analyses.
I use JMP as a database that can be computed directly.
Its sql-like statements are too powerful.
JSL is very powerful and very flexible.I have always insisted on using JSL to operate JMP.
Thanks for the post Mark. When I teach an Intro to JMP class I refer students to the Discovering JMP book that comes with JMP Help>Books>Discovering JMP and I point them to page 63, How is JMP Different from Excel? That seems to be very helpful to students.
I do not know if I can add much more to what has already been stated here, but here goes.
At my insistence, my employer purchased JMP so that I could perform the type(s) of analysis required for and by our most demanding client. While most government contracts are gray, this particular client wanted to go deeper than “what would happen if we implemented this?”¹ Or, as he put it, he wanted his study “to have a methodology and results that were defensible.” That meant following a statistical process: design of experiments, a data collection plan based on the DoE, modeling and analysis of the data, and inferencing the results from statistical analysis.
Prior to our acquisition of JMP, everything was performed in Excel. Depending on who was processing the data the workbooks that were created ran anywhere from mediocre to why. Despite the large user base, Excel is still at its core a powerful accounting ledger, so what our management incorrectly deemed to be statistical analysis was nothing more than summary analysis.² Proving this particular client’s need was not going to happen in Excel.
When we acquired a JMP license, my first thought was that a data table looks very similar to a spreadsheet, but I quickly found and realized that where Excel and other spreadsheet software typically have the cell as the base object; that is, each of the 6,871,947,674 cells is independent having no relation to any other cell unless otherwise defined by the user. As I had prior experience in SAS, it became rapidly apparent that a data table is a data model in waiting. As others have noted, columns serve as either independent variables (predictors) or dependent variables (responses), while the rows in a JMP data table are observations.
JMP’s paradigm prevents users from engaging in some of the more atrocious way in which people use Excel; such as putting multiple “tables” on a single worksheet instead of being treated as independent objects that should occupy their own worksheet. Excel while seemingly having a broad range of graphs, pales in comparison to the Graph Builder platform.
The various platforms offered in JMP have no equivalent in Excel outside of perhaps third-party add-ins. For people inexperienced with statistics, the Analysis ToolPak add-in that included, but not pre-installed in Excel reinforces the misconception that there is not much to performing statistics. Want to design an experiment? Can you employ various techniques to determine if a data set is from a normally distributed population? Need to perform analysis on non-parametric data? Do you want to perform pairwise analysis to determine the factors in your model that are significantly different? If you are using Excel, forget about addressing such needs.
Recently, a co-worker an I showed the president of the company how he could quickly explore data in JMP. The Distribution platform alone sold him, as he saw that with little effort he could rapidly see how data was distributed with histograms and box plots, as well as get quantiles and summary statistics. The capabilities of Graph Builder blew his mind. Our president later asked me if JMP can perform SQL queries, t which I answered, “Yes.” I provided him with the built-in PDF books included with JMP where he how to perform such queries.
Simply put, true data discovery and statistical analtysis software, such as JMP, are far better suited to analytics than the kludges that are applied in Excel. Excel is not designed to perform much beyond reporting summary statistics and basic graphs.
¹ It is often the case that government contracts are posed by non-technical persons at the management level. More often than not, the contractor needs to guide their client to a solid, and achievable, objective given the biding price and contract period of performance.
² One of the major issues data analysts and data scientists face is getting their supervisors and clients to realize that the generation of descriptive (summary) statistics is not the application of statistics, or more properly, statical science and data analytics that leads to statistical inference.
I also stumbled on to this thread and thank the previous responders for some great ideas. I offer my humble experiences. I do two things in my professional life:
1. I teach experimental design and sampling as means to gather data (and regression when there is no data collection plan) and a host of procedures to analyze the data. I follow my own guidance in the analysis of data: Practical, Graphical and lastly quantitative.
2. I analyze data sets form clients (these can take many forms).
I make a point that ~95% of my data analysis time is spent organizing the data into a structure/order so the data can be analyzed correctly. All questions that can be answered, conclusions that can be made, confidence in extrapolating the results, etc. are DEPENDENT on how the data was acquired (context).
All of my clients use Excel. I gave up on some emphatic DON"T USE THAT POS (piece of software). Software is a language. Many individuals speak Excel fluently and they are comfortable with it. JMP is a different language. Learning a new language can be terrifying and frustrating. Excel has its good points and its issues. Its good points include how extremely flexible it is, that is, of course, one of its bad points. I state ALL statistical analysis software requires discipline in its use (yes this includes dare I say Minitab...yikes). Over the course of working with my clients, it becomes clear the incredible capability of JMP. I demonstrate how organizing the data wrong leads to erroneous analysis. They naturally become convinced it is in their best interest to recognize how JMP performs analysis. I have not found one "event" to be the convincing argument, but repetition. Once they see this (admittedly it takes many reps), they now can use Excel with the knowledge of how it will need to be organized in JMP to perform the correct analysis.
A recent JMP Discussions reply from @markbailey regarding the KS test interpretation illustrates another differentiating global feature in JMP compared to your garden variety spreadsheet application. Interactive/online Help. If one is curious about a specific analysis platform report element and want to learn more, you don't have to hunt/peck/guess by keyword or table of contents search to learn more. All you need to do is go to the JMP main menu bar, select Tools -> Help, then hover your cursor over the element you want to learn more about and click on the element. You'll go directly to the most relevant content in the JMP documentation. Easy peasy.
To me, before we address technical differences (such as cells vs. variables), we have to keep in mind that there are "billions-and-billions" of spreadsheets out there created by "millions-and-millions" of long time users.
That is, Excel is an engrained Culture*, while JMP is ... ?
Any thoughts or experiences on how we address the Excel culture when promoting JMP?
* that comes automatically with Office software suites.
You have to demonstrate the value-add from a business perspective. The value is in time-saved in doing the analytics, not only doing the analytics faster, but far more powerfully (and easily). Start with graph builder. This platform alone is so incredibly powerful and really becomes easy to use after some patience and fortitude - it's drag and drop. JMP.com/learn is a great place to start. Short 5 minute videos are clear and precise to get the user base going. Seeing is beleiving. Visual analytics are the beginning and the end of every good data analysis.
Excel doesn't have this functionality. You can become a "pivot-table" expert with a few short tutorials in JMP in less than 10 minutes. You can become a "graphing" expert with just a few short tutorials in JMP in less than 10 minutes. With Excel it's much harder to get there, especially for the average user. And often the average user has the most direct insight into problems (from the hands-on perspective).
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